Welcome to the first lesson in the Use Vector Spatial data in Open Source Python - Geopandas module. This tutorial covers working with spatial data in vector format in Python. You will learn how to import, manipulate and map shapefile data in python. Finally you will learn how to reproject vector data into different coordinate reference systems.

About Vector Data

Vector data are composed of discrete geometric locations (x, y values) known as vertices that define the “shape” of the spatial object. The organization of the vertices determines the type of vector that you are working with. There are three types of vector data:

Points: Each individual point is defined by a single x, y coordinate. There can be many points in a vector point file. Examples of point data include: sampling locations, the location of individual trees or the location of plots.

Lines: Lines are composed of many (at least 2) vertices, or points, that are connected. For instance, a road or a stream may be represented by a line. This line is composed of a series of segments, each “bend” in the road or stream represents a vertex that has defined x, y location.

Polygons: A polygon consists of 3 or more vertices that are connected and “closed”. Thus the outlines of plot boundaries, lakes, oceans, and states or countries are often represented by polygons. Occasionally, a polygon can have a hole in the middle of it (like a doughnut), this is something to be aware of but not an issue you will deal with in this tutorial.

There are 3 types of vector objects: points, lines or polygons. Each object type has a different structure. Image Source: Colin Williams (NEON)

Data Tip: Sometimes, boundary layers such as states and countries, are stored as lines rather than polygons. However, these boundaries, when represented as a line, will not create a closed object with a defined “area” that can be “filled”.

Shapefiles: Points, Lines, and Polygons

Geospatial data in vector format are often stored in a shapefile format. Because the structure of points, lines, and polygons are different, each individual shapefile can only contain one vector type (all points, all lines or all polygons). You will not find a mixture of point, line and polygon objects in a single shapefile.

Objects stored in a shapefile often have a set of associated attributes that describe the data. For example, a line shapefile that contains the locations of streams, might contain the associated stream name, stream “order” and other information about each stream line object.

One Dataset - Many Files

A text file is often self contained. For example, one .csv file is composed of one unique file. Many spatial formats are composed of several files. A shapefile is created by 3 or more files, all of which must retain the same NAME and be stored in the same file directory, in order for you to be able to work with them.

Shapefile Structure

There are 3 key files associated with any and all shapefiles:

.shp: the file that contains the geometry for all features.

.shx: the file that indexes the geometry.

.dbf: the file that stores feature attributes in a tabular format.

These files need to have the same name and to be stored in the same directory (folder) to open properly in a GIS, R or Python tool.

Sometimes, a shapefile will have other associated files including:

.prj: the file that contains information on projection format including the coordinate system and projection information. It is a plain text file describing the projection using well-known text (WKT) format.

.sbn and .sbx: the files that are a spatial index of the features.

.shp.xml: the file that is the geospatial metadata in XML format, (e.g. ISO 19115 or XML format).

Data Management - Sharing Shapefiles

When you work with a shapefile, you must keep all of the key associated file types together. And when you share a shapefile with a colleague, it is important to zip up all of these files into one package before you send it to them!

Import Shapefiles

You will use the geopandas library to work with vector data in Python. You will also use matplotlib.pyplot to plot your data.

The first shapefile that you will open contains the point locations of plots where trees have been measured. To import shapefiles you use the geopandas function read_file(). Notice that you call the read_file() function using gpd.read_file() to tell python to look for the function within the geopandas library.

The CRS UTM zone 18N. The CRS is critical to interpreting the object extent values as it specifies units.

Spatial Data Attributes

Each object in a shapefile has one or more attributes associated with it. Shapefile attributes are similar to fields or columns in a spreadsheet. Each row in the spreadsheet has a set of columns associated with it that describe the row element. In the case of a shapefile, each row represents a spatial object - for example, a road, represented as a line in a line shapefile, will have one “row” of attributes associated with it. These attributes can include different types of information that describe objects stored within a shapefile. Thus, our road, may have a name, length, number of lanes, speed limit, type of road and other attributes stored with it.

Each spatial feature in an R spatial object has the same set of associated attributes that describe or characterize the feature. Attribute data are stored in a separate *.dbf file. Attribute data can be compared to a spreadsheet. Each row in a spreadsheet represents one feature in the spatial object. Image Source: National Ecological Observatory Network (NEON)

You can view the attribute table associated with our geopandas GeoDataFrame by simply typing the object name into the console (e.g., sjer_plot_locations). Or you can use the .head(3) function to only display the first 3 rows of the attribute table. The number in the .head() function represents the total number of rows that will be returned by the function.

# View the top 6 lines of attribute table of datasjer_plot_locations.head(6)

Plot_ID

Point

northing

easting

plot_type

geometry

0

SJER1068

center

4111567.818

255852.376

trees

POINT (255852.376 4111567.818)

1

SJER112

center

4111298.971

257406.967

trees

POINT (257406.967 4111298.971)

2

SJER116

center

4110819.876

256838.760

grass

POINT (256838.76 4110819.876)

3

SJER117

center

4108752.026

256176.947

trees

POINT (256176.947 4108752.026)

4

SJER120

center

4110476.079

255968.372

grass

POINT (255968.372 4110476.079)

5

SJER128

center

4111388.570

257078.867

trees

POINT (257078.867 4111388.57)

In this case, you have several attributes associated with our points including:

Plot_ID, Point, easting, geometry, northing, plot_type

Data Tip: The acronym, OGR, refers to the OpenGIS Simple Features Reference Implementation. Learn more about OGR.

The Geopandas Data Structure

Notice that the geopandas data structure is a data.frame that contains a geometry column where the x, y point location values are stored. All of the other shapefile feature attributes are contained in columns, similar to what you may be used to if you’ve used a GIS tool such as ArcGIS or QGIS.

Shapefile Metadata & Attributes

When you import the SJER_plot_centroids shapefile layer into Python the gpd.read_file() function automatically stores information about the data as attributes. You are particularly interested in the geospatial metadata, describing the format, CRS, extent, and other components of the vector data, and the attributes which describe properties associated with each individual vector object.

Spatial Metadata

Key metadata for all shapefiles include:

Object Type: the class of the imported object.

Coordinate Reference System (CRS): the projection of the data.

Extent: the spatial extent (geographic area that the shapefile covers) of the shapefile. Note that the spatial extent for a shapefile represents the extent for ALL spatial objects in the shapefile.

You can view shapefile metadata using the class(), .crs and .total_bounds methods:

# View object typetype(sjer_plot_locations)

geopandas.geodataframe.GeoDataFrame

# View the spatial extentsjer_plot_locations.total_bounds

array([ 254738.618, 4107527.074, 258497.102, 4112167.778])

The spatial extent of a shapefile or geopandas GeoDataFrame represents the geographic "edge" or location that is the furthest north, south east and west. Thus is represents the overall geographic coverage of the spatial object. Image Source: National Ecological Observatory Network (NEON)

# View CRS of objectsjer_plot_locations.crs

{'init': 'epsg:32611'}

The CRS for our data is epsg code: 32611. You will learn about CRS formats and structures in a later lesson but for now a quick google search reveals that this CRS is: UTM zone 11 North - WGS84.

sjer_plot_locations.geom_type

0 Point
1 Point
2 Point
3 Point
4 Point
5 Point
6 Point
7 Point
8 Point
9 Point
10 Point
11 Point
12 Point
13 Point
14 Point
15 Point
16 Point
17 Point
dtype: object

How Many Features Are In Your Shapefile?

You can view the number of features (counted by the number of rows in the attribute table) and feature attributes (number of columns) in our data using the pandas .shape method. Note that the data are returned as a vector of two values:

(rows, columns)

Also note that the number of columns includes a column where the geometry (the x, y coordinate locations) are stored.

Plot a Shapefile

Next, you can visualize the data in your Pythongeodata.frame object using the .plot() method. Notice that you can create a plot using the geopandas base plotting using the syntax:

dataframe_name.plot()

You can call .plot() without setting up a figure and axis object like this:

sjer_plot_locations.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x120868518>

You can quickly plot a geopandas dataframe using the .plot() method. You do not have to setup an axis or figure object to create this quick plot.

However in general it is good practice to setup an axis object so you can plot different layers together. When you do that you need to provide the plot function with the axis object that you want it to plot on. Below, you define the axis as ax, here:

fig, ax = plt.subplots(figsize = (10,10))

You then plot the data and provide the ax= argument with the ax object.

You can plot the data by feature attribute and add a legend too. Below you add the following plot arguments to your geopandas plot:

column: the attribute column that you want to plot your data using

categorical=True: set the plot to plot categorical data - in this case plot types.

legend: add a legend

markersize: increase or decrease the size of the points or markers rendered on the plot

cmap: set the colors used to plot the data

title add a title to your plot.

and fig size if you want to specify the size of the output plot.

fig,ax=plt.subplots(figsize=(10,10))# Plot the data and add a legendsjer_plot_locations.plot(column='plot_type',categorical=True,legend=True,figsize=(10,6),markersize=45,cmap="Set2",ax=ax)# Add a titleax.set_title('SJER Plot Locations\nMadera County, CA')plt.show()

Spatial plot of SJER plot locations using Geopandas with a legend and title.

Change Plot Colors & Symbols

You can use the cmap argument to adjust the colors of our plot. Below you used a colormap that is a part of the matplotlib colormap library.

Test your knowledge: Import Line & Polygon Shapefiles

Using the steps above, import the data/week5/california/madera-county-roads/tl_2013_06039_roads and data/week5/california/SJER/vector_data/SJER_crop.shp shapefiles into Python. Call the roads object sjer_roads and the crop layer sjer_crop_extent.

Answer the following questions:

What type of Python spatial object is created when you import each layer?

What is the CRS and extent for each object?

Do the files contain, points, lines or polygons?

How many spatial objects are in each file?

Plot Multiple Shapefiles Together With Geopandas

You can plot several layers on top of each other using the geopandas .plot method. To do this, you:

Define the ax variable just as you did above to add a title to our plot.

Then you add as many layers to the plot as you want using geopandas .plot() method.

Notice below

ax.set_axis_off() is used to turn off the x and y axis and

plt.axis('equal') is used to ensure the x and y axis are uniformly spaced.

fig,ax=plt.subplots(figsize=(10,10))# First setup the plot using the crop_extent layer as the base layersjer_crop_extent.plot(color='lightgrey',edgecolor='black',alpha=.5,ax=ax)# Add another layer using geopandas syntax .plot, and calling the ax variable as the axis argumentsjer_plot_locations.plot(column='plot_type',categorical=True,marker='*',legend=True,markersize=50,cmap='Set2',ax=ax)# Clean up axesax.set_title('SJER Plot Locations\nMadera County, CA')ax.set_axis_off()plt.axis('equal')